标签：#att

Hackers are targeting internet-connected Siemens controllers used in water facilities around the United States.

I put top-rated USB car chargers to the test for fast charging, value, heat, and safety. These are the best I found.

The U.S. phone provider escaped a large-scale breach of its network after identifying Chinese-backed hackers early on.

The cyberattack at CareCloud resulted in one of the largest reported data breaches in the U.S. healthcare industry this year.

The ChatGPT maker says its upcoming Astra model may have reached “critical” cyber capabilities, prompting it to halt a significant number of training runs while it tightens internal safeguards.

Design Patterns: Reusable Solutions to Recurring Problems A practical guide to classic design patterns in C#/.NET — Factory, Singleton, Repository, Strategy, and Mediator — covering what problem each one actually solves, working implementations, common .NET-specific variations, and honest guidance on when each pattern earns its complexity versus when it's unnecessary ceremony. Table of Contents Introduction Factory Pattern Singleton Pattern Repository Pattern Strategy Pattern Mediator Pattern How These Patterns Combine in Practice Patterns vs. Over-Engineering Common Pitfalls Quick Reference Table Conclusion Introduction Design patterns are named, reusable solutions to problems that recur often enough across software projects that giving them a shared name and shape is genuinely useful — not because the specific code is copy-pasteable, but because the name lets developers communicate a design intent quickly ("just make it a Strategy") instead of re-explaining the same structural idea from scratch every time. This guide covers five of the most commonly used patterns in .NET codebases, with working C# examples, and — consistent with this series' recurring theme — honest guidance on when each pattern is solving a genuine problem versus adding structure a simpler solution wouldn't need. // A pattern name compresses a whole design conversation into one word "Just inject an IPaymentStrategy and pick the implementation based on the payment method" // ← Strategy "Wrap the whole multi-step checkout process behind a single mediator call" // ← Mediator 1. Factory Pattern The problem: object creation logic that doesn't belong at the call site // ❌ The caller needs to know about every concrete shipping provider and how to construct each one IShippingProvider provider = order . Region switch { "US" => new UpsShippingProvider ( apiKey , region ), "EU" => new DhlShippingProvider ( apiKey , endpoint ), "APAC" => new FedExShippingProvider ( apiKey , credentials ), _ => throw new NotS

Battery materials startup Anthro Energy has broken ground on a Louisville factory to make electrolytes, including those for solid-state batteries.

A bad cutout rarely announces itself as a bad cutout. The car lands on a new backdrop, the paint looks clean, then a thin piece is gone. An antenna. A tire lip. The dark seam under a rocker panel. The complaint that comes back is never technical. The vehicle looks wrong. I wanted the last correction stage to fix fuzzy edges without handing it the whole car to rewrite. That sounds like a small distinction. It stops being small the first time a model improves one boundary and quietly damages another. So the rule is physical. Edit the uncertain strip. Leave the settled area alone. This is Part 2. Part 1, "Negative Space Is a Label", was about supervision: what the pixels beside an object teach a model, and why a shadow touching a tire has to be labeled as evidence against foreground. This one moves from training to runtime. A mask already exists. Where is a learned stage allowed to act? 1. The contract lives in the band CarSegNet is the research implementation here. Its pipeline module splits the route by media type, and the docstring says the design more clearly than any diagram I could draw after the fact. Stills run SAM 3 text concept, then NSJ alpha, then composite. A detector box prompt and a depth prior are optional inputs. Video runs SAM 3.1 multiplex propagation, per-frame NSJ with temporal handling, a depth-parallax plate, composite, encode. The list matters less than the handoff. SAM gives a semantic prior. NSJ receives a trimap band. The compositor receives a matte only after the prior and the refiner have each done bounded work. flowchart TD image[Vehicle Image] segment[Concept Mask] trimap[Trimap Band] refiner[NSJ Alpha Refiner] depth[Depth Prior] composite[Showroom Composite] frozen[Prior Frozen Outside Band] image --> segment segment --> trimap trimap --> refiner image --> depth depth --> refiner refiner --> composite segment -.-> frozen frozen --> composite The diagram is a contract. It is not a model zoo. The refiner edits the uncertain strip. The sema

The hacks at shipping companies used to mail out hardware wallets puts crypto owners at greater risk of real-world attacks.

Jendrik Jördening shares practical engineering strategies for integrating LLMs into production pipelines. He discusses overcoming non-determinism, restricting schemas, separating semantic text extraction from deterministic code, and validating choices using discriminator models. Learn how to structure LLMs with an MVC approach to ensure database integrity, observability, and system reliability. By Jendrik Jördening

Over the last couple of weeks, hackers have targeted and broken into the systems of several water plants in the United States. Here’s what we know and don’t know about this wave of attacks allegedly carried out by the Iranian government.

The new order sweeps away decades of existing U.S. cybersecurity policy prohibiting private companies from conducting 'hack back' attacks or offensive cyber operations.

The data was scraped and exfiltrated from 2,500 users of a compromised AI package.

Mesh, an AI-powered contacts app and relationship manager from Automattic, is now an Android app.

Form Energy has landed Google and Crusoe as customers. Now, it has raised $750 million to expand manufacturing to deliver its massive, 100-hour batteries.

For a long time, my mental model of Dependency Injection looked like this: create an interface,...

The Road to Battlefield competition is now in its second year, and its purpose is to give founders across Central Eurasia a direct route to Startup Battlefield

Security researchers found that in less than 60 seconds, they could open a hatch on a plane’s exterior, plug in a tiny device, and redirect the aircraft’s autopilot or sabotage its flight plan.

Researchers say it took fewer than 20 prompts for a public AI tool to find a flaw (now fixed) allowing anyone on a Zoom call to hijack another participants’ device.

A car mask can pass review and still teach the model to keep the wrong pixels. The outline looks clean. The bumper is inside. The wheels are inside. Then the trained network holds onto the dark patch under the tires, because the label treated that patch as part of the vehicle's visual neighborhood. Training stays quiet. Production gets loud the first time a listing photo drags a strip of the old lot onto a new backdrop. AutoLensAI turns dealer photography into listing-ready vehicle media. This installment follows the earlier pieces on segmentation and image provenance, then narrows to one question: how do I teach a matting model that the shadow touching a tire is evidence against foreground rather than a faint version of it? 1. The failure arrives without an error message Vehicle matting estimates which pixels belong to the vehicle, at finer boundary resolution than segmentation gives. Tires, rocker panels, glossy showroom floors, and the halo under a lowered front lip are where a pretty binary mask does its damage. Two cases cause most of it. A cast shadow can touch rubber and still sit outside the object. A reflection can match paint color exactly and still belong to the floor. Both look like they belong to the car in a thumbnail. Neither belongs to it in geometry. A binary target has no vocabulary for that distinction. Every pixel is in or out, so the annotator's only lever is where to put the line. Push the line outward and shadow becomes vehicle. Pull it inward and the wheel arch loses its edge. Neither answer says the thing that matters, which is that some exterior pixels are ordinary background and some are adversarial background sitting one pixel from the object. The model learns the difference anyway. It learns it wrong, because nothing in the supervision ever separated the two. 2. Three states, not two The supervision contract uses three: state meaning training treatment vehicle body, glass, wheels, trim, and visible geometry foreground loss hard negative